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Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

Bernd Opitz
University of Mannheim, Germany

Cäcilia Zirn
University of Mannheim, Germany

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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16

Linköping Electronic Conference Proceedings 85:23, s. 253-265

NEALT Proceedings Series 16:23, p. 253-265

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Publicerad: 2013-05-17

ISBN: 978-91-7519-589-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

People tend to have various opinions about topics. In discussions; they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are; or help a company to analyze how well people like their new products. In this work; we develop an approach for recognizing agreement and disagreement. However; this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases; machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique; we achieve an accuracy of 72.85%. Besides; we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features.

Nyckelord

Text Classification; Agreement; Disagreement; Opinion Mining

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